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  Policy Gradient Methods

Peters, J., & Bagnell, J. (2010). Policy Gradient Methods. In C. Sammut, & G. Webb (Eds.), Encyclopedia of Machine Learning (pp. 774-776). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BD40-C Version Permalink: http://hdl.handle.net/21.11116/0000-0002-951C-7
Genre: Book Chapter

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 Creators:
Peters, J1, 2, Author              
Bagnell, JA, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized control policy by a variant of gradient descent. These methods belong to the class of policy search techniques that maximize the expected return of a policy in a fixed policy class, in contrast with traditional value function approximation approaches that derive policies from a value function. Policy gradient approaches have various advantages: they enable the straightforward incorporation of domain knowledge in policy parametrization and often an optimal policy is more compactly represented than the corresponding value function; many such methods guarantee to convergence to at least a locally optimal policy; the methods naturally handle continuous states and actions and often even imperfect state information. The counterveiling drawbacks include difficulties in off-policy settings, the potential for very slow convergence and high sample complexity, as well as identifying local optima that are not globally optimal.

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 Dates: 2010-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-0-387-30164-8_640
BibTex Citekey: 6074
 Degree: -

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Title: Encyclopedia of Machine Learning
Source Genre: Book
 Creator(s):
Sammut, C, Editor
Webb, GI, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 774 - 776 Identifier: ISBN: 978-0-387-30164-8